{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- <center> -->\n",
    "<h1> Using MOMENT for Classification </h1>\n",
    "<!-- </center> -->\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "### 1. A Quick Introduction to Classification\n",
    "### 2. Loading MOMENT\n",
    "### 3. Inputs and Outputs\n",
    "### 4. Two Approaches for Time Series Classification\n",
    "#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4.1 Classification Dataset\n",
    "#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4.2 Unsupervised Representation Learning using MOMENT\n",
    "#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4.3 Learning a Statistical ML Classifier on MOMENT Embeddings\n",
    "#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4.4 Visualize the Embeddings\n",
    "#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4.5 Results Interpretation\n",
    "#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4.6 Fully Supervised Learning using Classification Head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. A Quick Introduction to Classification\n",
    "\n",
    "Classification is a popular time series modeling task which involves assigning a categorical label to a time series sub-sequence. For example, in the context of [electrocardiogram (ECG) recordings](https://en.wikipedia.org/wiki/Electrocardiography), classification may entail distinguishing between normal and anomalous heartbeats. In this tutorial, we will explore two ways to use MOMENT to solve any time series classification problem. Mathematically, the time series classification problem is defined as follow:\n",
    "\n",
    "**Problem**: Given a time-series $T = [x_1, ..., x_L], \\ x_i \\in \\mathbb{R}^{C}$ of length $L$ with $C$ channels (sensors or variables) with $n$ attributes, the classification problem is to predict a class label $m \\in \\{0, \\dots, M\\}$ for each time series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading MOMENT\n",
    "\n",
    "We will first install the MOMENT package, load some essential packages and the pre-trained model. \n",
    "\n",
    "MOMENT can be loaded in 4 modes: (1) `reconstruction`, (2) `embedding`, (3) `forecasting`, and (4) `classification`.\n",
    "\n",
    "In the `reconstruction` mode, MOMENT reconstructs input time series, potentially containing missing values. We can solve imputation and anomaly detection problems in this mode. This mode is suitable for solving imputation and anomaly detection tasks. During pre-training, MOMENT is trained to predict the missing values within uniformly randomly masked patches (disjoint sub-sequences) of the input time series, leveraging information from observed data in other patches. As a result, MOMENT comes equipped with a pre-trained reconstruction head, enabling it to address imputation and anomaly detection challenges in a zero-shot manner! Check out the `anomaly_detection.ipynb` and `imputation.ipynb` notebooks for more details!\n",
    "\n",
    "In the `embedding` model, MOMENT learns a $d$-dimensional embedding (e.g., $d=1024$ for `MOMENT-1-large`) for each input time series. These embeddings can be used for clustering and classification. MOMENT can learn embeddings in a zero-shot setting! Check out `representation_learning.ipynb` notebook for more details! \n",
    "\n",
    "The `forecasting` and `classification` modes are used for forecasting and classification tasks, respectively. In these modes, MOMENT learns representations which are subsequently mapped to the forecast horizon or the number of classes, using linear forecasting and classification heads. Both the forecasting and classification head are randomly initialized, and therefore must be fine-tuned before use. Check out the `forecasting.ipynb` notebook for more details!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install numpy pandas scikit-learn matplotlib tqdm\n",
    "!pip install git+https://github.com/moment-timeseries-foundation-model/moment.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from momentfm import MOMENTPipeline\n",
    "\n",
    "model = MOMENTPipeline.from_pretrained(\n",
    "    \"AutonLab/MOMENT-1-large\", \n",
    "    model_kwargs={\n",
    "        'task_name': 'classification',\n",
    "        'n_channels': 1,\n",
    "        'num_class': 5\n",
    "    }, # We are loading the model in `classification` mode\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MOMENTPipeline(\n",
      "  (normalizer): RevIN()\n",
      "  (tokenizer): Patching()\n",
      "  (patch_embedding): PatchEmbedding(\n",
      "    (value_embedding): Linear(in_features=8, out_features=1024, bias=False)\n",
      "    (position_embedding): PositionalEmbedding()\n",
      "    (dropout): Dropout(p=0.1, inplace=False)\n",
      "  )\n",
      "  (encoder): T5Stack(\n",
      "    (embed_tokens): Embedding(32128, 1024)\n",
      "    (block): ModuleList(\n",
      "      (0): T5Block(\n",
      "        (layer): ModuleList(\n",
      "          (0): T5LayerSelfAttention(\n",
      "            (SelfAttention): T5Attention(\n",
      "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (relative_attention_bias): Embedding(32, 16)\n",
      "            )\n",
      "            (layer_norm): T5LayerNorm()\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "          (1): T5LayerFF(\n",
      "            (DenseReluDense): T5DenseGatedActDense(\n",
      "              (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
      "              (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
      "              (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
      "              (dropout): Dropout(p=0.1, inplace=False)\n",
      "              (act): NewGELUActivation()\n",
      "            )\n",
      "            (layer_norm): T5LayerNorm()\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1-23): 23 x T5Block(\n",
      "        (layer): ModuleList(\n",
      "          (0): T5LayerSelfAttention(\n",
      "            (SelfAttention): T5Attention(\n",
      "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "            )\n",
      "            (layer_norm): T5LayerNorm()\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "          (1): T5LayerFF(\n",
      "            (DenseReluDense): T5DenseGatedActDense(\n",
      "              (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
      "              (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
      "              (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
      "              (dropout): Dropout(p=0.1, inplace=False)\n",
      "              (act): NewGELUActivation()\n",
      "            )\n",
      "            (layer_norm): T5LayerNorm()\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (final_layer_norm): T5LayerNorm()\n",
      "    (dropout): Dropout(p=0.1, inplace=False)\n",
      "  )\n",
      "  (head): ClassificationHead(\n",
      "    (dropout): Dropout(p=0.1, inplace=False)\n",
      "    (linear): Linear(in_features=1024, out_features=5, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model.init()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Inputs and Outputs\n",
    "Let's begin by performing a forward pass through MOMENT and examining its outputs!\n",
    "\n",
    "MOMENT takes 3 inputs: \n",
    "1. An input time series of length $T=512$ timesteps and $C$ channels, and \n",
    "2. Two optional masks, both of length $T=512$. \n",
    "    - The input mask is utilized to regulate the time steps or patches that the model should attend to. For instance, in the case of shorter time series, you may opt not to attend to padding. To implement this, you can provide an input mask with zeros in the padded locations.  \n",
    "    - The second mask, referred to simply as mask, denotes masked or unobserved values. We employ mask tokens to replace all patches containing any masked time step (for further details, refer to Section 3.2 in our [paper](https://arxiv.org/abs/2402.03885)). MOMENT can attend to these mask tokens during reconstruction.\n",
    "    - By default, all time steps are observed and attended to.\n",
    "\n",
    "MOMENT returns a `TimeseriesOutputs` object. Since this is a classification task, it returns both `logits` and `embeddings` of the input. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimeseriesOutputs(forecast=None,\n",
      "                  anomaly_scores=None,\n",
      "                  logits=tensor([[ 0.0054, -0.1209, -0.0395,  0.1871, -0.0193],\n",
      "        [ 0.0057, -0.1151, -0.0023,  0.1507, -0.0448],\n",
      "        [ 0.0439, -0.0712, -0.0671,  0.1883, -0.0195],\n",
      "        [ 0.0189, -0.0545, -0.0252,  0.2035, -0.0749],\n",
      "        [ 0.0583, -0.1014,  0.0112,  0.1829, -0.0766],\n",
      "        [ 0.0325, -0.1154, -0.0024,  0.1657, -0.0949],\n",
      "        [ 0.0630, -0.0939, -0.0441,  0.2058, -0.0037],\n",
      "        [ 0.0553, -0.0805, -0.0147,  0.2005, -0.1364],\n",
      "        [ 0.0288, -0.0762,  0.0027,  0.1209, -0.0341],\n",
      "        [ 0.0401, -0.0919, -0.0257,  0.2198, -0.0882],\n",
      "        [ 0.0253, -0.1203,  0.0121,  0.1671, -0.0499],\n",
      "        [ 0.0307, -0.1093,  0.0070,  0.2172, -0.0815],\n",
      "        [ 0.0453, -0.1003,  0.0103,  0.1956, -0.1095],\n",
      "        [ 0.0094, -0.0625,  0.0009,  0.1681, -0.0705],\n",
      "        [-0.0099, -0.1043,  0.0430,  0.2029, -0.0838],\n",
      "        [-0.0140, -0.0543, -0.0010,  0.1875, -0.0346]],\n",
      "       grad_fn=<AddmmBackward0>),\n",
      "                  labels=None,\n",
      "                  input_mask=None,\n",
      "                  pretrain_mask=None,\n",
      "                  reconstruction=None,\n",
      "                  embeddings=tensor([[[-7.8550e-02, -7.1337e-02, -1.3411e-01,  ..., -2.3279e-01,\n",
      "          -5.4562e-02, -1.5337e-02],\n",
      "         [ 8.4697e-03,  7.9522e-02, -1.0164e-01,  ..., -1.7917e-01,\n",
      "          -8.2168e-02, -9.8388e-02],\n",
      "         [-1.3629e-01,  3.8646e-02,  3.3849e-02,  ...,  1.6358e-01,\n",
      "          -8.8710e-02, -8.9114e-02],\n",
      "         ...,\n",
      "         [-7.4161e-02,  1.5846e-02,  2.4004e-02,  ...,  1.7857e-03,\n",
      "          -1.0105e-01, -1.5736e-04],\n",
      "         [-1.6388e-01, -7.7960e-02, -5.8958e-02,  ...,  9.7286e-02,\n",
      "          -3.4287e-02, -1.2971e-01],\n",
      "         [-1.5411e-01, -4.8779e-02, -1.1698e-01,  ...,  2.4156e-01,\n",
      "           4.8474e-02, -5.1270e-02]],\n",
      "\n",
      "        [[-1.3473e-01, -4.0715e-02, -1.6295e-01,  ...,  6.0605e-02,\n",
      "           4.1747e-03, -7.4778e-02],\n",
      "         [-1.3528e-01,  2.4071e-02, -3.6464e-02,  ..., -1.3189e-01,\n",
      "           5.0611e-02, -1.3449e-01],\n",
      "         [-1.0416e-01,  4.2741e-02, -2.2191e-02,  ..., -8.8145e-03,\n",
      "          -5.6086e-02, -7.1998e-02],\n",
      "         ...,\n",
      "         [-1.5038e-01, -1.9282e-02, -3.0696e-02,  ...,  1.9900e-01,\n",
      "          -9.3449e-02,  7.7880e-04],\n",
      "         [-8.3262e-02,  4.6080e-02, -8.4522e-02,  ...,  9.0550e-02,\n",
      "          -5.7416e-02, -4.2587e-02],\n",
      "         [-1.6831e-01, -8.0188e-02, -5.9064e-02,  ..., -3.5411e-02,\n",
      "           5.8221e-02,  2.9940e-02]],\n",
      "\n",
      "        [[-9.4501e-02, -5.1645e-02, -4.6168e-02,  ...,  9.6817e-02,\n",
      "          -4.2179e-02, -6.8871e-02],\n",
      "         [-1.3712e-01,  9.6630e-02, -5.3519e-02,  ...,  1.2936e-01,\n",
      "           2.3030e-02, -9.3553e-02],\n",
      "         [-1.5233e-01,  8.7018e-02, -2.7894e-02,  ...,  2.4901e-03,\n",
      "          -1.4768e-02,  2.8179e-02],\n",
      "         ...,\n",
      "         [-1.9878e-01,  6.7920e-02,  6.1479e-02,  ...,  3.2768e-01,\n",
      "          -1.6533e-01,  5.7174e-03],\n",
      "         [-2.5274e-01,  2.2309e-04, -8.8157e-02,  ...,  1.2434e-01,\n",
      "           4.7176e-02, -8.7995e-02],\n",
      "         [-8.7663e-02,  1.2260e-01, -6.5819e-02,  ...,  8.7058e-02,\n",
      "           2.1121e-03, -2.9503e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.8820e-01, -5.7481e-04, -2.1575e-02,  ...,  1.7245e-01,\n",
      "          -1.4308e-02, -1.0691e-01],\n",
      "         [-2.1219e-01, -7.4999e-02, -6.6669e-02,  ...,  2.5129e-02,\n",
      "           3.4424e-02, -6.5015e-02],\n",
      "         [-3.3788e-02,  1.9747e-01, -4.7911e-02,  ..., -3.0637e-02,\n",
      "          -1.3910e-01, -3.8855e-02],\n",
      "         ...,\n",
      "         [-1.5573e-01,  6.0649e-03,  9.0227e-02,  ...,  2.2014e-01,\n",
      "          -1.6391e-02,  3.8866e-02],\n",
      "         [-1.5559e-01,  1.5065e-01,  9.7744e-03,  ...,  1.1742e-01,\n",
      "          -6.1568e-03,  2.1977e-02],\n",
      "         [-1.2866e-01, -4.1004e-02, -6.0112e-02,  ...,  1.5329e-01,\n",
      "           1.5886e-03,  3.3016e-02]],\n",
      "\n",
      "        [[-2.0693e-01, -4.1878e-02, -1.2262e-01,  ...,  3.8894e-02,\n",
      "           5.9940e-02,  3.9674e-02],\n",
      "         [-1.2299e-01,  5.2629e-02, -5.6827e-02,  ...,  3.6507e-02,\n",
      "          -3.4795e-02, -3.3506e-02],\n",
      "         [-7.7146e-02,  4.4283e-02, -1.3396e-01,  ..., -9.9458e-02,\n",
      "           5.4780e-02,  2.7519e-02],\n",
      "         ...,\n",
      "         [-1.2529e-01, -5.9690e-03, -1.5684e-01,  ...,  1.2761e-01,\n",
      "           1.7508e-02, -8.2980e-02],\n",
      "         [-2.4433e-01, -6.6164e-02, -2.3471e-01,  ...,  5.9825e-02,\n",
      "           8.6809e-02, -2.4683e-03],\n",
      "         [-1.9808e-01, -1.4974e-02, -1.1679e-01,  ..., -5.8217e-02,\n",
      "           9.1142e-02,  2.1526e-02]],\n",
      "\n",
      "        [[-1.5163e-01,  8.3666e-02, -1.4513e-01,  ..., -7.2024e-02,\n",
      "           3.4731e-02, -3.4348e-02],\n",
      "         [-2.7293e-03,  2.6094e-02, -1.6367e-01,  ...,  1.2242e-02,\n",
      "           4.2293e-03, -5.7497e-02],\n",
      "         [-1.3783e-01, -7.1307e-02, -2.2442e-01,  ..., -2.4569e-01,\n",
      "           9.0989e-02, -6.8389e-02],\n",
      "         ...,\n",
      "         [-1.1822e-01,  7.2743e-02,  3.3828e-02,  ...,  2.8441e-02,\n",
      "          -4.4584e-02,  2.1479e-02],\n",
      "         [-5.5470e-02,  9.7812e-02, -2.3222e-02,  ...,  2.2601e-01,\n",
      "          -6.4763e-02, -6.9306e-02],\n",
      "         [-1.9683e-01,  6.9445e-02, -1.5974e-01,  ..., -1.2880e-01,\n",
      "           1.7285e-01, -6.9577e-02]]], grad_fn=<MeanBackward1>),\n",
      "                  metadata='mean',\n",
      "                  illegal_output=False)\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "import torch\n",
    "\n",
    "# takes in tensor of shape [batchsize, n_channels, context_length]\n",
    "x = torch.randn(16, 1, 512)\n",
    "output = model(x)\n",
    "pprint(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# backward\n",
    "# [batch_size, num_classes]\n",
    "logits = output.logits\n",
    "\n",
    "# [batch_size, ]\n",
    "predicted_labels = logits.argmax(dim=1)\n",
    "predicted_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: The classification head is randomly initialized, so these predictions are random. We must train the classification head to get reasonable results. Below we show a quick example of how we can fine-tune the classification head."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Two Approaches for Time Series Classification\n",
    "\n",
    "We can use MOMENT to solve the classification problem in two ways: (1) unsupervised representation learning, and (2) fully supervised learning using classification head. \n",
    "\n",
    "### Unsupervised Representation Learning\n",
    "In this setting, we use MOMENT to embed time series data (see `representation_learning.ipynb`). Next, we train a Support Vector Machine (SVM) classifier using these embeddings as features and labels. This setting is common in field of unsupervised representation learning, where the goal is to learn meaningful time series representations without any labeled data (see [TS2Vec](https://arxiv.org/pdf/2106.10466) for a recent example). The quality of these representations are evaluated based on the performance of the downstream classifier (in this case, SVM). This is also the setting that we consider in our [paper](https://arxiv.org/abs/2402.03885). \n",
    "\n",
    "### Fully Supervised Learning using Classification Head\n",
    "In this approach, we replace MOMENT's reconstruction head with a classification head, which maps the patch-level represenations to the number of classes present in a dataset. This classification head is randomly initialized and can be trained using cross-entropy loss and labeled data. We will not explore this approach in detail in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Classification Dataset\n",
    "\n",
    "For these experiments, we will use the ECG5000 dataset from the [UCR Classification Archive](https://www.timeseriesclassification.com/description.php?Dataset=ECG5000). The original dataset is a 20-hour long ECG record (chf07) from the BIDMC Congestive Heart Failure Database (CHFDB) available on [PhysioNet](https://physionet.org/about/database/). This data was pre-processed in two steps: (1) extract each heartbeat, (2) make each heartbeat equal length using interpolation. Following these steps, a subset of 5,000 heartbeats were randomly chosen and automatically annotated into 5 classes including normal and abnormal heartbeats.\n",
    "\n",
    "We'll start by reading and pre-processing this dataset using the `ClassificationDataset` class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from momentfm.data.classification_dataset import ClassificationDataset\n",
    "\n",
    "train_dataset = ClassificationDataset(data_split='train')\n",
    "test_dataset = ClassificationDataset(data_split='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "idx = np.random.randint(0, len(train_dataset))\n",
    "heartbeat_start = np.argmax(train_dataset[idx][1])\n",
    "heartbeat = train_dataset[idx][0].squeeze()[heartbeat_start:]\n",
    "label = train_dataset[idx][2]\n",
    "plt.plot(heartbeat, c='darkblue')\n",
    "plt.title(f\"idx={idx} | label={label}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Unsupervised Representation Learning using MOMENT\n",
    "\n",
    "In this setting, we use MOMENT to embed all training and testing time series. Then we train a statistical classifier (e.g. support vector machine) using the embeddings of the training time series as features and training labels. We will show that MOMENT can learn meaningful representations in a zero-shot setting, which can be used to train powerful statistical classifiers. \n",
    "\n",
    "Let's embed the train and test datasets! We'll proceed as follows: \n",
    "First, we will write a simple function `get_embedding` which will iterate over the training and testing datasets, and embed each time series. Then we will use the `fit_svm` function to fit a support vector machine (SVM) model using these embeddings as features and training labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=False, drop_last=False)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=False, drop_last=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "def get_embedding(model, dataloader):\n",
    "    embeddings, labels = [], []\n",
    "    with torch.no_grad():\n",
    "        for batch_x, batch_masks, batch_labels in tqdm(dataloader, total=len(dataloader)):\n",
    "            batch_x = batch_x.to(\"cuda\").float()\n",
    "            batch_masks = batch_masks.to(\"cuda\")\n",
    "\n",
    "            output = model(batch_x, input_mask=batch_masks) # [batch_size x d_model (=1024)]\n",
    "            embedding = output.embeddings\n",
    "            embeddings.append(embedding.detach().cpu().numpy())\n",
    "            labels.append(batch_labels)        \n",
    "\n",
    "    embeddings, labels = np.concatenate(embeddings), np.concatenate(labels)\n",
    "    return embeddings, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For unsupervised representation learning, MOMENT can be initialized in both `embedding` and `classification` mode.\n",
    "\n",
    "```python\n",
    "\n",
    "model = MOMENTPipeline.from_pretrained(\n",
    "    \"AutonLab/MOMENT-1-large\", \n",
    "    model_kwargs={'task_name': 'embedding'}, # We are loading the model in `embedding` mode\n",
    ")\n",
    "model.init()\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:07<00:00,  1.03it/s]\n",
      "100%|██████████| 71/71 [00:43<00:00,  1.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 1024) (500,)\n",
      "(4500, 1024) (4500,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model.to(\"cuda\").float()\n",
    "\n",
    "train_embeddings, train_labels = get_embedding(model, train_dataloader)\n",
    "test_embeddings, test_labels = get_embedding(model, test_dataloader)\n",
    "\n",
    "print(train_embeddings.shape, train_labels.shape)\n",
    "print(test_embeddings.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Learning a Statistical ML Classifier on MOMENT Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/zfsauton2/home/mgoswami/anaconda3/envs/moment/lib/python3.11/site-packages/sklearn/model_selection/_split.py:737: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.94\n"
     ]
    }
   ],
   "source": [
    "from momentfm.models.statistical_classifiers import fit_svm\n",
    "\n",
    "clf = fit_svm(features=train_embeddings, y=train_labels)\n",
    "\n",
    "y_pred_train = clf.predict(train_embeddings)\n",
    "y_pred_test = clf.predict(test_embeddings)\n",
    "train_accuracy = clf.score(train_embeddings, train_labels)\n",
    "test_accuracy = clf.score(test_embeddings, test_labels)\n",
    "\n",
    "print(f\"Train accuracy: {train_accuracy:.2f}\")\n",
    "print(f\"Test accuracy: {test_accuracy:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Visualize the Embeddings\n",
    "\n",
    "Next, let's visualize the embeddings that MOMENT is learning using Principal Component Analysis (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "test_embeddings_manifold = PCA(n_components=2).fit_transform(test_embeddings) \n",
    "\n",
    "plt.title(f\"ECG5000 Test Embeddings\", fontsize=20)\n",
    "plt.scatter(\n",
    "    test_embeddings_manifold[:, 0], \n",
    "    test_embeddings_manifold[:, 1],\n",
    "    c=test_labels.squeeze()\n",
    ")\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Results Interpretation \n",
    "#### MOMENT Learns Meaningful Representations without Dataset-specific Fine-tuning\n",
    "\n",
    "Here we can see that the first two principal components of the representations that MOMENT is learning can distinguish normal and abnormal heartbeats denoted by different colors. This can explain MOMENT + SVM's promising classification accuracy. Note that these represenations are learnt zero-shot. Even without dataset-specific fine-tuning, MOMENT learns distinct representations of different classes of heartbeats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 Fully Supervised Learning using Classification Head\n",
    "\n",
    "\n",
    "We can also perform classification by attaching a randomly-initialized classification head to MOMENT and fine-tuning it. Here's an example code to fine-tune MOMENT with a classification head:\n",
    "\n",
    "```python\n",
    "\n",
    "# Remember MOMENT must be initialized in `classification` mode\n",
    "model = MOMENTPipeline.from_pretrained(\n",
    "    \"AutonLab/MOMENT-1-large\", \n",
    "    model_kwargs={\n",
    "        'task_name': 'classification',\n",
    "        'n_channels': 1,\n",
    "        'num_class': 5\n",
    "    },\n",
    ")\n",
    "\n",
    "# Define a data loader \n",
    "train_dataloader = DataLoader(dataset, batch_size=64, shuffle=True) \n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "\n",
    "for data, labels in train_dataloader:\n",
    "    # forward [batch_size, n_channels, forecast_horizon]\n",
    "    output = model(data)\n",
    "\n",
    "    # backward\n",
    "    loss = criterion(output.logits, labels)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    print(f\"loss: {loss.item():.3f}\")\n",
    "```"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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